Samuel John Cooper

Lecturer

Imperial College London

United Kingdom

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Samuel John Cooper is a Lecturer at Imperial College London, UK. His research interests encompass materials science, particularly in the areas of microstructural optimization, X-ray chemical imaging, and the development of machine learning tools for battery modeling. His recent articles include topics such as the prediction of microstructural representativity, the effects of space charge on reaction kinetics, and the application of generative AI in Li-ion battery design.

Articles (21)

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2025

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2025

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2021

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2020

Collaborators (9)

Charles Monroe

Professor

University of Oxford

UNITED KINGDOM

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Jamie Michael Foster

Academic, Professor, Professor of Applied and Industrial Mathematics

University of Portsmouth

UNITED KINGDOM

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Zhao Liu

University of Washington

UNITED STATES

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Ferran Brosa Planella

Associate Professor

University of Warwick

UNITED KINGDOM

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Lorenzo Picinali

Reader in Audio Experience Design

Imperial College London

UNITED KINGDOM

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Lisa Heymann

IWW Rheinisch-Westfälisches Institut für Wasserforschung GmbH

GERMANY

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Andres Galvis

Academic, Lecturer, Lecturer

University of Portsmouth

UNITED KINGDOM

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Aidan Hogg

Assistant Professor/Lecturer in Computer Science

Queen Mary University of London

UNITED KINGDOM

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David Howey

University of Oxford

UNITED KINGDOM

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Samuel John Cooper

Research Interests

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About

Articles (21)

Prediction of Microstructural Representativity From A Single Image

nDTomo: a modular Python toolkit for X-ray chemical imaging and tomography

HRTF Upsampling With a Generative Adversarial Network Using a Gnomonic Equiangular Projection

Li-ion battery design through microstructural optimization using generative AI

SAMBA: A Trainable Segmentation Web-App with Smart Labelling

Investigating the Effect of the Separation of Scales in Reduced Order Battery Modelling: Implications on the Validity of the Newman Model

Materials science in the era of large language models: a perspective

Materials science in the era of large language models: a perspective

Space Charge Effects on the Reaction Kinetics of Metal Exsolution and the Coalescence of Exsolved Nanoparticles

Thermal stability and coalescence dynamics of exsolved metal nanoparticles at charged perovskite surfaces

TauFactor 2: A GPU accelerated python tool for microstructural analysis

Fusion of Complementary 2D and 3D Mesostructural Datasets Using Generative Adversarial Networks

Artefact removal from micrographs with deep learning based inpainting

Artefact removal from micrographs with deep learning based inpainting†

Review of parameterisation and a novel database (LiionDB) for continuum Li-ion battery models

Machine-Learning-Driven Advanced Characterization of Battery Electrodes

MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN

Methods—Kintsugi Imaging of Battery Electrodes: Distinguishing Pores from the Carbon Binder Domain using Pt Deposition

MATBOX: An Open-source Microstructure Analysis Toolbox for microstructure generation, segmentation, characterization, visualization, correlation, and meshing

Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion

Combining Electrochemical, Fluid Dynamic, and Imaging Analyses to Understand the Effect of Electrode Microstructure and Electrolyte on Redox Flow Batteries

Collaborators (9)

Charles Monroe

Jamie Michael Foster

Zhao Liu

Ferran Brosa Planella

Lorenzo Picinali

Lisa Heymann

Andres Galvis

Aidan Hogg

David Howey

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